2009 IEEE Congress on Evolutionary Computation最新文献_第9页

Genetic algorithm based fuzzy multiple regression for the nocturnal Hypoglycaemia detection 基于遗传算法的模糊多元回归夜间低血糖检测

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586315

S. Ling, H. Nguyen, Kit Yan Chan

引用次数: 3

Time-varying constraints and other practical problems in real-world scheduling applications 时变约束和实际调度应用中的其他实际问题

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586415

A. Mohais, M. Ibrahimov, S. Schellenberg, Neal Wagner, Z. Michalewicz

引用次数: 1

Incorporation of imprecise goal vectors into evolutionary multi-objective optimization 不精确目标向量在进化多目标优化中的应用

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586413

L. Rachmawati, D. Srinivasan

引用次数: 7

Meta-learning for data summarization based on instance selection method 基于实例选择方法的数据汇总元学习

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5585986

K. Smith‐Miles, R. Islam

引用次数: 11

Automated discovery of vital knowledge from Pareto-optimal solutions: First results from engineering design 从帕累托最优解中自动发现重要知识:工程设计的第一个结果

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586501

Sunith Bandaru, K. Deb

引用次数: 38

Differential evolution with dynamic adaptation of mutation factor applied to inverse heat transfer problem 基于变异因子动态适应的微分进化方法应用于换热逆问题

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586000

V. Mariani, Vagner Jorge Neckel, L. D. Afonso, L. Coelho

{"title":"Differential evolution with dynamic adaptation of mutation factor applied to inverse heat transfer problem","authors":"V. Mariani, Vagner Jorge Neckel, L. D. Afonso, L. Coelho","doi":"10.1109/CEC.2010.5586000","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586000","url":null,"abstract":"In this paper a Modified Differential Evolution (MDE) is proposed and its performance for solving the inverse heat transfer problem is compared with Genetic Algorithm with Floating-point representation (GAF) and classical Differential Evolution (DE). The inverse analysis of heat transfer has some practical applications, for example, the estimation of radioactive and thermal properties, such as the conductivity of material with and without the temperatures dependence of diffusive processes. The inverse problems are usually formulated as optimization problems and the main objective becomes the minimization of a cost function. MDE adapts a concept originally proposed in particle swarm optimization design for the dynamic adaptation of mutation factor. Using a piecewise function for apparent thermal conductivity as a function of the temperature data, the heat transfer equation is able to estimate the unknown variables of the inverse problem. The variables that provide the beast least squares fit between the experimental and predicted time-temperatures curves were obtained. Numerical results for inverse heat transfer problem demonstrated the applicability and efficiency of the MDE algorithm. In this application, MDE approach outperforms the GAF and DE best solutions.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"58 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87643413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An investigation on sampling technique for multi-objective restricted Boltzmann machine 多目标受限玻尔兹曼机采样技术研究

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586469

Vui Ann Shim, K. Tan, J. Y. Chia

{"title":"An investigation on sampling technique for multi-objective restricted Boltzmann machine","authors":"Vui Ann Shim, K. Tan, J. Y. Chia","doi":"10.1109/CEC.2010.5586469","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586469","url":null,"abstract":"Estimation of distribution algorithms are increasingly gaining research interest due to their linkage information exploration feature. Two main mechanisms which contribute towards the success of the algorithms are probabilistic modeling and sampling method. Recent attention has been directed towards the development of probabilistic building technique. However, research on the sampling approach is less developed. Thus, this paper carries out an investigation on sampling technique for a novel multi-objective estimation of distribution algorithm — multi-objective restricted Boltzmann machine. Two variants of a new sampling technique based on energy value of the solutions in the trained network are proposed to improve the efficiency of the algorithm. Probabilistic information which is usually clamped into marginal probability distribution may hinder the algorithm in producing solutions that have high linkage dependency between variables. The proposed approach will overcome this limitation of probabilistic modeling in restricted Boltzmann machine. The empirical investigation shows that the proposed algorithm gives promising result in term of convergence and convergence rate.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"5 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87901028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach 用双目标和惩罚函数混合方法快速准确地求解约束优化问题

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586543

K. Deb, Rituparna Datta

{"title":"A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach","authors":"K. Deb, Rituparna Datta","doi":"10.1109/CEC.2010.5586543","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586543","url":null,"abstract":"Evolutionary algorithms are modified in various ways to solve constrained optimization problems. Of them, the use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective, has received a significant attention. Classical penalty function approach is another common methodology which requires an appropriate knowledge of the associated penalty parameter. In this paper, we combine a bi-objective evolutionary approach with the penalty function methodology in a manner complementary to each other. The bi-objective optimization approach provides a good estimate of the penalty parameter, while the unconstrained penalty function approach using classical means provides the overall hybrid algorithm its convergence property. We demonstrate the working of the procedure on a two-variable problem and then solve a number of standard numerical test problems from the EA literature. In all cases, our proposed hybrid methodology is observed to take one or more orders of magnitude smaller number of function evaluations to find the constrained minimum solution accurately. To the best of our knowledge, no previous evolutionary constrained optimization algorithm has reported such a fast and accurate performance on the chosen problems.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"128 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88100582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 64

Generating a novel sort algorithm using Reinforcement Programming 利用强化规划生成一种新的排序算法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586457

S. White, T. Martinez, G. Rudolph

引用次数: 1

Constraint handling procedure for multiobjective particle swarm optimization 多目标粒子群优化的约束处理方法

2009 IEEE Congress on Evolutionary Computation Pub Date : 2010-07-18 DOI: 10.1109/CEC.2010.5586394

G. Yen, W. Leong

引用次数: 9